TP-GNN: Continuous Dynamic Graph Neural Network for Graph Classification

Dynamic networks are data structures that represent the interactions among various entities in real-world systems, with their topology and node properties evolving over time. However, prevailing approaches typically derive node embeddings through aggregating temporal neighbor nodes of adjacent several hops, thus failing to capture the long temporal dependencies in dynamic networks. Furthermore, existing research on dynamic networks focuses on node- and edge-level tasks, lacking the support of graph-level tasks. To address the limitations of current approaches, this paper proposes TP-GNN, a novel continuous dynamic graph neural network model intended for graph classification in dynamic networks, which offers two primary advantages: (1) TP-GNN captures the long temporal dependencies via a novel message-passing method based on the information flow among the nodes, and (2) it learns the network evolution process from edge order for accurate dynamic network analytics. We evaluate the performance of TP-GNN in five datasets, including a new dataset we created from a Java software project. The results show that our method outperforms state-of-the-art approaches in graph classification with an average improvement of 4.91% in terms of$F_{1}$Score11Codes and dataset are available at https://github.com/Jie-0828/TP-GNN..